Dimas Fanny Hebrasianto Permadi, Moch Zawaruddin Abdullah


The purpose of this research is to apply the Convolutional Neural Network (CNN) method in the field of Computer Vision. The CNN algorithm is a combination of Neural Network and Multilayer Perceptron which uses a convolution approach to extract features. The CNN technique was used to identify an animal dataset that has 16,130 images divided into three categories: Cats, Dogs and Wild. This study aims to recognize facial images of animals belonging to the category of Cats, Dogs or Wild Animals which resemble the derivatives of cats or dogs such as Lions, Tigers, Hyenas, Wolves and so on. Comparing to learning rate and epoch, the results are 10e-4 and 60 respectively. Utilizing random images from the datasets, learning rate and epoch may achieve an accuracy of about 97.22% or 116.33 out of 120 images. When using images taken outside of the datasets, the accuracy may be as high as 77.78% or 93.33 out of 120 images.

Full Text:



N. Shahdadpuri, “Real Image of Computer Vision Application and its Impact : Future and Challenges,” Wesley. J. Res., vol. 13, no. 53, pp. 62–75, 2020.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020.

J. Gross, J. Breitenbach, H. Baumgartl, and R. Buettner, “High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks,” Proc. 54th Hawaii Int. Conf. Syst. Sci., pp. 3416–3425, 2021.

W. Rahmaniar and A. Hernawan, “Real-Time Human Detection Using Deep Learning on Embedded Platforms : A Review,” J. Robot. Control, vol. 2, no. 6, pp. 462–468, 2021, doi: 10.18196/jrc.26123.

M. R. Alwanda, R. Putra, K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” J. Algoritm., vol. 1, no. 1, 2020.

A. S. Riyadi, I. Puspa, and S. Widayati, “KLASIFIKASI CITRA ANJING DAN KUCING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ( CNN ),” in Seminar Nasional Teknologi Informasi dan Komunikasi, 2021, vol. 5, pp. 2–6.

N. Azahro Choirunisa, T. Karlita, and R. Asmara, “Deteksi Ras Kucing Menggunakan Compound Model Scaling Convolutional Neural Network,” Technomedia J., vol. 6, no. 2, pp. 236–251, 2021, doi: 10.33050/tmj.v6i2.1704.

M. R. Effendi, “Sistem Deteksi Wajah Jenis Kucing Dengan Image Classification Menggunakan Opencv,” J. Teknol. Inform. dan Komput., vol. 4, no. 1, pp. 27–35, 2018, doi: 10.37012/jtik.v4i1.283.

M. A. Pangestu and H. Bunyamin, “Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model,” J. Tek. Inform. dan Sist. Inf., vol. 4, pp. 337–344, 2018.

K. O. Lauw et al., “Identifikasi Jenis Anjing Berdasarkan Gambar Menggunakan Convolutional Neural Network Berbasis Android,” J. Infra, vol. 8, no. 2, pp. 37–43, 2020.

A. Hossain and S. Sajib Alam, “Classification of Image using Convolutional Neural Network (CNN),” Glob. J. Comput. Sci. Technol. D Neural Artif. Intell., vol. 19, no. 2, 2019.


D. Jha, A. Yazidi, M. A. Riegler, D. Johansen, and D. Johansen, “LightLayers : Parameter Efficient Dense and Convolutional Layers for Image Classification,” arXiv Prepr. arXiv2101.02268, no. Ml, pp. 1–12, 2021.

E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.

C. K. Dewa and A. L. Fadhilah, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 1, pp. 83–94, 2018, doi: 10.22146/ijccs.31144.

B. Boehmke and B. Greenwell, Hands-On Machine Learning with R. 2019.

P. Singh and A. Manure, Learn TensorFlow 2.0. 2020.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. Teknol. Inf. Indones., vol. 3, pp. 49–56, 2018.

Wulan Anggraini, “Deep Learning Untuk Deteksi Wajah Yang Berhijab Menggunakan Algoritma Convolutional Neural Network (Cnn) Dengan Tensorflow,” Deep Learn. Untuk Deteksi Wajah Yang Berhijab Menggunakan Algoritm. Convolutional Neural Netw. Dengan Tensorflow, vol. 28, no. 2, pp. 1–43, 2020, [Online]. Available:

I. K. M. Jais, A. R. Ismail, and S. Q. Nisa, “Adam Optimization Algorithm for Wide and Deep Neural Network,” Knowl. Eng. Data Sci., vol. 2, no. 1, p. 41, 2019, doi: 10.17977/um018v2i12019p41-46.

Y. Ho and S. Wookey, “The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling,” IEEE Access, vol. 8, pp. 4806–4813, 2020, doi: 10.1109/ACCESS.2019.2962617.

A. Krizhevsky and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” pp. 1–9.

A. Panja, J. J. Christy, and Q. M. Abdul, “An Approach to Skin Cancer Detection using Keras and Tensorflow,” J. Phys. Conf. Ser., vol. 1911, no. 1, p. 012032, 2021, doi: 10.1088/1742-6596/1911/1/012032.

A. Koul, S. Ganju, and M. Kasam, Practical Deep Learning for Cloud, Mobile, and Edge, vol. 53, no. 9. O’Reilly Media, 2018.

Y. N. U. R. Fuadah, I. D. Ubaidullah, N. U. R. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. Adnan, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” vol. 10, no. 3, pp. 728–741, 2022.

R. Agustina, R. Magdalena, and N. O. R. K. Caecar, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,” Elkomika, vol. 10, no. 2, pp. 446–457, 2022, [Online]. Available:

A. Alamsyah, B. Prasetiyo, M. F. Al Hakim, and F. D. Pradana, “Prediction of COVID-19 Using Recurrent Neural Network Model,” Sci. J. Informatics, vol. 8, no. 1, pp. 98–103, 2021, doi: 10.15294/sji.v8i1.30070.

M. A. Abu, N. H. Indra, A. H. A. Rahman, N. A. Sapiee, and I. Ahmad, “A study on image classification based on deep learning and tensorflow,” Int. J. Eng. Res. Technol., vol. 12, no. 4, pp. 563–569, 2019.



  • There are currently no refbacks.